Modern machine learning capabilities are rapidly changing and improving how some of the most complex and data-intensive computing problems are solved. A performance of a machine learning model is governed mainly in the manner(s) in which the machine learning model is trained and based on the hyperparameters of the machine learning model set prior to the training of the model. As referenced in passing the hyperparameters of the machine learning models are parameters whose values are set prior to the commencement of the machine learning process rather than derived by the machine learning model during training. Example include the number of trees in a random forest or the number of hidden layers in a deep neural net. Adjusting the values of the hyperparameters of a machine learning model by any amount typically results in a large impact on a performance of the machine learning model.
However, many existing machine learning models are not implemented with optimal hyperparameters well-suited for achieving the best predictive performances. Rather, the many existing machine learning models are implemented with default hyperparameters that have not been optimized for a specific computing problem for which the machine learning models are being used.
Additionally, any existing system that enables optimization of hyperparameters of a machine learning model typically includes an extremely complex interface that may require significant coding capabilities and comprehension of the underlying software and hardware components of the system. Thus, making it difficult to efficiently and effectively perform optimizations and subsequent improvements of the machine learning models.
Thus, there is a need in the machine learning field to create an improved optimization platform to test and improve machine learning models (e.g., in-product machine learning models) and an associated Application Program Interface that enables developers to efficiently and effectively interact with a robust system implementing the evaluation framework. The embodiments of the present application described herein provide technical solutions that address, at least, the need described above, as well as the technical deficiencies of the state of the art described throughout the present application.